@ManuKumar

Cofounder/CEO @HiHello 👋 Cofounder @CartaInc 🥧 Chief Firestarter @K9Ventures 🦮 OG Pre-Seed Investor @lyft @twilio @auth0 @lucidchart @everlaw @forethought_ai

Palo Alto, CA
Joined April 2008
Dr. Manu Kumar 👋🏽 retweeted
At the beginning of this year, big portions of @databricks started running on Genie. When finance started doing all their work with Genie, we noticed that they were combining Genie with a Live Cloud Spreadsheet called Row Zero. This combination is really powerful. We met the Row Zero team and were blown away. Today, we're excited to announce that we've agreed to acquire Row Zero. Stay tuned for an awesome experience of Genie + Row Zero: databricks.com/company/newsr…
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Dr. Manu Kumar 👋🏽 retweeted
Replying to @minchoi
Colossus 1 is 150k H100, 50k H200 and 30k GB200. Colossus 2 is 110k GB200 and 440k GB300. Another 220k GB300 will be fully operational next week and another 220k in November. If we get lucky, yet another 220k GB300 by late December.
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Just watching the animation and the manual is awesome.
Opus 5.5 designing LEGO 👀 I asked it to design a Microduck I can build with real LEGO pieces. It: > designed it life-size using 1113 real LEGO parts > verified: 3,204 connections, 0 collisions, every step buildable, centre of mass inside the feet 🤯 > made a 141-page LEGO-style booklet (237 steps) > priced every piece in the browser and prepared the orders on BrickLink
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Dr. Manu Kumar 👋🏽 retweeted
Tesla drivers know FSD is better than human driving because while our cars are driving us around we see all the stupid and ridiculous maneuvers humans make
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Dr. Manu Kumar 👋🏽 retweeted
Today we announced the Claude-led discovery of a molecular machine that we suspect could represent a new gene editing mechanism. Its precise function, biotechnological utility (if any), or level of significance is not yet clear, but at minimum it is work I would have been proud to do as a PhD student. The work was done mostly, though not entirely, by Claude: our life sciences team suggested a broad area of research, Claude read through the literature and a bunch of genome data and discovered something interesting, then Claude proposed experiments to verify the discovery and our team carried them out. It’s easy to dismiss this as a one-off or curiosity, but we’ve repeatedly seen a pattern where AI performance in new intellectual domains goes from weak to superhuman in a matter of a few years. In 2023 models struggled to do math at the level of an average high-school student. In 2024 they started to do well on math competitions for the best high-schoolers in the country, in 2025 they started to solve minor open problems, in early 2026 more significant open problems, and in late 2026 they are beginning to solve the top few open problems in all of mathematics. We believe AI for biology is on a similar exponential trend. The main difference between biology and mathematics, of course, is that math can be done purely theoretically, while biology requires experimentation. Some have used this to draw the conclusion that AI’s utility in biology will be limited. We think this is wrong. As we’ve demonstrated today, humans can collaborate with AI to perform the experiments, validate key results in a few weeks and, if necessary, work with the AI to iterate on what they find. Eventually it may even be possible for Claude itself to safely perform the experiments by autonomously controlling lab equipment, with appropriate safeguards in place, but we aren’t doing that today (our lab is also a BSL1/BSL2 facility that doesn't handle materials dangerous to humans). More broadly, biomedical advancement has many stages — from fundamental biology discoveries, to translational research, to drug discovery, clinical trials, and finally the actual delivery of medicines and health care to patients. We are also interested in these later stages, but even simply accelerating the first stage of fundamental biological discoveries has the potential to speed up and broaden the entire pipeline. Improving our understanding of biology and sharpening biologists’ tools can drive forward all of the later stages, for example by identifying new drug targets, finding new therapeutic modalities, allowing for more precise measurement, and speeding up the experimental loop which itself further accelerates our understanding of biology. This will not in itself speed up clinical trial times, but if it succeeds it could greatly increase the number of promising candidates that go into the pipeline — an increase in throughput even though latency remains. In Machines of Loving Grace, I wrote about AI’s potential to “cure most diseases in 5-10 years” — a goal that sounds impossible, but one I believe is just barely possible if AI is applied to every stage of the pipeline. The first step is showing that AI can first help with, and then drive, biological discoveries. Claude’s discovery is the latest in a line of related prior work that goes back decades, beginning with systems like CRISPR, and continuing with discoveries like the bridge recombinase and VIPR in the past few years. Recently, there has been heightened interest in systems based on reverse transcriptase (RT) enzymes, the enzyme underlying the system Claude identified. And most recently, a Stanford team working independently described a novel RT system with an associated non-coding array that is in some ways similar to the one Claude found, though they are distinct systems that evolved independently from each other. I believe that we’re at the very beginning of finding such systems and developing them into powerful tools for biotechnology. I’m proud of the resources Anthropic has invested in accelerating the public benefits of AI through the life sciences, and we’re aiming both to grow our life sciences team and to work with other scientists to extend this approach to a broad range of problems. If you have a proposal for a research collaboration or are interested in joining our life sciences team, please reach out.
Claude has discovered a previously unknown enzyme system hidden in the DNA of bacteriophages. Beside the enzyme’s gene sits a long array of repeating DNA—a structure that looks somewhat similar to CRISPR. We don’t yet understand what this system does, but only a handful of known systems share its features, and all of them are able to cut, copy, and paste DNA. Historically, the discovery of such programmable systems has helped revolutionize medicine. CRISPR, for instance, is now the foundation of genetic medicines. But it will take much more work to learn what this system does, and whether it can be put to similar use. Read more: anthropic.com/news/claude-di…
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Good thread with recent @bot updates!
Some recent quality-of-life improvements to Grok Bot. A cleaner interface for working with your Bots.
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Dr. Manu Kumar 👋🏽 retweeted
pacing the frontier: "we should slow down AI development" the frontier: Claude Opus 5.5, GPT-6 Sol, GPT-6 Luna, Grok 4.7, Xiaomi MiMo-V2.6-Pro launched this week
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Dr. Manu Kumar 👋🏽 retweeted
Introducing Claude Opus 5.5, the first model in our new Claude 5.5 family. It performs at the level of Claude Fable 5.1 for most tasks, and costs 40% less to run than Opus 5.
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Dr. Manu Kumar 👋🏽 retweeted
hey @googlecalendar calendar spam is getting out of control. There's no reason for calendar invites that are marked as spam to appear on the calendar. Please fix.
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Dr. Manu Kumar 👋🏽 retweeted
Grok @Bot now in your Tesla!
.@Grok in your Tesla can now do meaningful work for you With Connectors, you can manage your inbox, clean up your calendar, or talk through existing files/chat/tasks – all hands-free
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Dr. Manu Kumar 👋🏽 retweeted
Grok 4.7 is here. It's a notable improvement over Grok 4.6 at the same price and speed.
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Dr. Manu Kumar 👋🏽 retweeted
The life of a button... 🔊
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Great post by @venkyganesan. I don’t understand today’s venture market. 🤷🏽‍♂️
A few thoughts on the current state of venture capital. When the Music Is Playing In July 2007, a few weeks before the credit markets seized up, Chuck Prince, then the CEO of Citigroup, gave an interview to the Financial Times. The line everyone remembers is this one: "As long as the music is playing, you've got to get up and dance." He was mocked for it for years afterward, and he lost his job a few months later. But I have come to think he was saying something honest. He wasn't claiming the music would play forever. He was admitting that he couldn't sit down while it was still going, and neither could anyone else in his seat. I've been thinking about that quote a lot lately, because right now is the most disorienting period in venture capital I can remember, and I have been doing this for a while. Here is what makes it disorienting. It's not that things are bad. Some things are spectacular. We have companies in our portfolio growing faster than anything I have seen in my career, and I don't say that lightly. At the same time, we have companies with no revenue, no product, and a founding team you could fit in a conference room raising billions of dollars at valuations of $10 to $50 billion. Both of these things are true at once, and if you try to reason about them with the same framework you will drive yourself crazy. Two ideas have helped me make sense of it. Neither is mine. The first is reflexivity, which George Soros has been writing about since the 1980s. In most of life, perception follows reality: the weather is what it is, and your opinion of it changes nothing. In markets, it runs the other way too. Prices change what participants believe, and what participants believe changes the prices. The feedback loop can run for a long time, and while it's running it looks exactly like progress. Here is how reflexivity is playing out in AI. Full disclosure: Menlo is an investor in Anthropic, so read the following with that in mind. People watched a frontier lab go from a $4 billion valuation to $18 billion, then $60 billion, then $180 billion, then $380 billion, and now something close to a trillion. They drew the obvious conclusion: that is what a neo lab looks like. So the next neo lab gets priced off that path, not off anything it has built. Then it gets marked up in a subsequent round, and the markup itself becomes the proof. Look at Thinking Machines. Look at Reflection. At that point valuation has stopped being an output of the metrics and has become the metric. Nobody is discounting cash flows. They are discounting the last round. Soros is very clear about one thing, and it's the part people skip: you cannot know when or how a reflexive process ends. You only know that it does. Every one of them has. The second idea is Chuck Prince's, and it explains why smart people keep dancing even when they can see the loop for what it is. As far as I can tell, there are two groups on the dance floor. The first group got in early. Firms like ours were in some of these AI companies before the numbers got silly, and the paper gains are enormous. When you are sitting on gains like that, you start to feel like you're playing with house money. I have been around long enough to know that house money is the most dangerous kind, because you don't respect it the way you respect money you had to earn. The second group missed the early rounds and knows it. Their LPs know it too. So they are trying to make up for lost time by writing very large checks very late, which is the one strategy almost guaranteed to turn a missed opportunity into a real loss. House money on one side, FOMO on the other, and reflexivity feeding both. That's the whole story. Everyone has a reason to keep dancing, and the reasons are different, which is why nobody can talk anyone else off the floor. So what do you do? The instinct in our business is to answer with company identification: just pick the right neo lab and you'll be fine. I think that's the trap. When price has become the signal, being right about the company is not enough, because you can be right about the company and still be wrong about the price by a factor of ten. The public-market investors I admire figured this out a long time ago. They spend as much time on how much to own as on what to own. The winners in venture over the next decade will be the firms that treat portfolio composition and position sizing as seriously as they treat sourcing. How much of the fund is in companies whose valuation rests on the last round rather than on revenue? What happens to the portfolio if the reflexive loop breaks next year instead of in five? Those are not exciting questions. They are the ones that will matter. The music will stop. It always does. Dance if you must, but know where the chairs are.
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Dr. Manu Kumar 👋🏽 retweeted
Solving a Rubik's Cube with graph theory.
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Dr. Manu Kumar 👋🏽 retweeted
Supercharger Site Maps live in the latest Tesla App
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Anyone managed to figure out how to connect @bot to @box? There doesn’t seem to be a simple/straightforward path for this per Grok Bot.
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Dr. Manu Kumar 👋🏽 retweeted
Grok Bot can talk now.
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If you’re not raising Series A and Series B in one month and your Series C in the following month are you even a real Silicon Valley company?
It would be quite normal for a hot startup to raise a seed round from Benchmark, then go to Sequoia, then to Index/KP, and then do a growth round with Greenoaks. Instinct fundraising has been the opposite order, which is fascinating!
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Dr. Manu Kumar 👋🏽 retweeted
You can also now make decks, docs, and designs in your conversation. Draft the one-pager in Claude Docs, turn it into a deck with Claude Slides, and mock up a matching visual in Claude Design, all from one place.
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Dr. Manu Kumar 👋🏽 retweeted
FSD feature request: add a Record Path button. I need the Cybertruck to reverse the full length of my driveway into the garage. That’s the only way it fits. FSD always pulls in forward or parks on the street, so I cancel every time. Let me record the exact path once. FSD learns it, then applies its own safety layer on top of that recorded route. This would help a lot of people with tight, long, or odd driveways/situations— not just me. @aelluswamy @elonmusk
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